gregcaporaso

This depends on biom version >= 2.1.5, < 2.2.0 and vsearch >= 1.7.0.

Please note that all of this is highly experimental. I'm keeping these notes as I work with this approach. For published work, I still recommend using standard pipelines, such as those in QIIME 1.9.1.

$ biom --version
biom, version 2.1.5

$ vsearch --version
vsearch v1.7.0_osx_x86_64, 16.0GB RAM, 8 cores

gregcaporaso

gregcaporaso

gregcaporaso

gregcaporaso

gregcaporaso

gregcaporaso

gregcaporaso

#!/usr/bin/env python
# File created on 01 Dec 2011
from __future__ import division

__author__ = "Greg Caporaso"
__copyright__ = "Copyright 2011, The QIIME project"
__credits__ = ["Greg Caporaso"]
__license__ = "GPL"
__version__ = "1.3.0-dev"
__maintainer__ = "Greg Caporaso"

gregcaporaso

#!/usr/bin/env python

from sys import argv
from random import random
from cogent.parse.fastq import MinimalFastqParser
from cogent.draw.distribution_plots import generate_box_plots
from qiime.quality import ascii_to_phred33
from qiime.util import qiime_open

def fastq_quality_plots(fastq_records,

gregcaporaso

#!/usr/bin/env python
# Authors: Greg Caporaso, John Chase
# Questions: Contact gregcaporaso@gmail.com

# Step 1: Generate lists of pre/post sample ids on a per-individual basis
# qiime.group.extract_per_individual_states_from_sample_metadata 
# will let you generate a dict of individual id to (pre sample-id, post sample-id)

# Step 2: Extract distances for pre/post sample ids
# qiime.parse.parse_distmat_to_dict